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New method improves nonlinear model parameter estimation for battery health prediction

By PulseAugur Editorial · [1 sources] ·

A new paper introduces a regularized iterative generalized least squares method for identifying nonlinear phenomenological models, particularly useful in fields like state of health prediction for lithium-ion batteries where parameters can be difficult to estimate reliably. The method incorporates an automated approach to optimize a ridge regression hyper-parameter at each iteration using information-theoretic measures, demonstrating rapid convergence. This technique is designed to handle heteroscedastic and serially correlated data, with simulations confirming its effectiveness. AI

IMPACT This methodology could improve the accuracy of predictive models in various scientific and engineering domains, potentially impacting AI applications that rely on accurate parameter estimation from experimental data.

RANK_REASON The cluster contains a single academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method improves nonlinear model parameter estimation for battery health prediction

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The cluster contains a single academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mark Cary, Charles Bokor ·

    Regularised Iterative Generalised Least Squares with Optimal Selection of the Hyper-Parameter for Identifying Nonlinear Phenomenological Models

    arXiv:2608.18742v1 Announce Type: cross Abstract: In some fields currently dominated by empirical approaches, such as state of health (SoH) prediction for lithium-ion batteries, phenomenological models motivated by quasi-physical thinking contain parameters to be estimated from e…

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